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第34届中国控制与决策会议

作者： Anna Wang Yingjie Xiong Tao Zhang College Of Information Science and Engineering Northeastern University School of Electrical Engineering Dalian University of Technology Shenyang Institute of Automation Chinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Key Laboratory of Networked Control Systems Chinese Academy of Sciences

In the position sensorless control strategy of permanent magnet synchronous motor(PMSM),the back-EMF method based on sliding mode observer(SMO) is favored because of its *** this paper,a SMO based on the piecewise sigmoid function is *** on the back-EMF observation error,the gain adaptation of the SMO is realized,which effectively reduces the high-frequency chattering problem of the conventional *** low-pass filter of the conventional SMO is improved,and an adaptive filter based on the back-EMF low-pass filter is *** to the back-EMF observation error,the cut-off frequency of the low-pass filter is changed in real-time,and the estimation of rotor position is *** Matlab/Simulink simulation verification,the control strategy is feasible and achieves a better control effect.

关键词： Sensorless control SMO Adaptive gain and Adaptive filter

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CPnP: Consistent Pose Estimator for Perspective-n-Point Problem with Bias Elimination

arXiv

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arXiv 2022年

作者： Zeng, Guangyang Chen, Shiyu Mu, Biqiang Shi, Guodong Wu, Junfeng School of Data Science Chinese University of Ho ng Kong Shenzhen Shenzhen China Key Laboratory of Systems and Control Institute of Systems Science Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW2008 Australia

The Perspective-n-Point (PnP) problem has been widely studied in both computer vision and photogrammetry societies. With the development of feature extraction techniques, a large number of feature points might be available in a single shot. It is promising to devise a consistent estimator, i.e., the estimate can converge to the true camera pose as the number of points increases. To this end, we propose a consistent PnP solver, named CPnP, with bias elimination. Specifically, linear equations are constructed from the original projection model via measurement model modification and variable elimination, based on which a closed-form least-squares solution is obtained. We then analyze and subtract the asymptotic bias of this solution, resulting in a consistent estimate. Additionally, Gauss-Newton (GN) iterations are executed to refine the consistent solution. Our proposed estimator is efficient in terms of computations—it has O(n) computational complexity. Experimental tests on both synthetic data and real images show that our proposed estimator is superior to some well-known ones for images with dense visual features, in terms of estimation precision and computing time. Copyright © 2022, The Authors. All rights reserved.

关键词： Linear equations

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Coordinate Descent Clustering with Trace Difference Criterion for Joint Clustering and Feature Extraction

SSRN

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SSRN 2023年

作者： Wang, Quan Wang, Fei Li, Zhongheng Wang, Zheng Nie, Feiping Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University Shaanxi Xi’an710049 China Beijing Key Laboratory of Intelligent Space Robotic Systems Technology and Applications Beijing Institute of Spacecraft System Engineering Beijing100094 China School of Software Engineering Xi’an Jiaotong University Xi’an710049 China Northwestern Polytechnical University Shaanxi Xi’an710072 China

Joint clustering and dimensionality reduction methods are a promising solution to clustering due to its scalability to high-dimensional data. Some methods leverage trace ratio criterion and attain clusters by borrowing the K-means algorithm. However, trace ratio criterion has no close-formed solution for the discriminative projection matrix and the K-means algorithm has a limited capacity to handle the many-cluster problem. In this paper, Coordinate Descent Clustering with Trace Difference criterion (CDCTD) is proposed for joint clustering and feature extraction. Formulating the objective function as a direct trace difference criterion containing a balance parameter, CDCTD harmonizes between-cluster scatter maximization and within-cluster scatter minimization by the balance parameter. Using the direct trace difference criterion, CDCTD can straightforward solve for the discriminative projection matrix and avoid obtaining a poor discriminative projection matrix in the iterative manner when a bad cluster start is given. CDCTD uses the coordinate descent method for clustering, improving the ability to address the many-cluster problem. Extensive experiments show that CDCTD has achieved significant performance improvements compared to previous trace ratio criterion related joint clustering and feature extraction methods, and also outperformed other clustering methods in most cases. © 2023, The Authors. All rights reserved.

关键词： Feature extraction

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Safe-VLN: Collision Avoidance for Vision-and-Language Navigation of Autonomous Robots Operating in Continuous Environments

arXiv

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arXiv 2023年

作者： Yue, Lu Zhou, Dongliang Xie, Liang Zhang, Feitian Yan, Ye Yin, Erwei The College of Engineering Peking University Beijing100871 China Defense Innovation Institute Academy of Military Sciences Beijing100071 China Tianjin Artificial Intelligence Innovation Center Tianjin300450 China Shenzhen518055 China The Department of Advanced Manufacturing and Robotics The State Key Laboratory of Turbulence and Complex Systems College of Engineering Peking University Beijing100871 China

The task of vision-and-language navigation in continuous environments (VLN-CE) aims at training an autonomous agent to perform low-level actions to navigate through 3D continuous surroundings using visual observations and language instructions. The significant potential of VLN-CE for mobile robots has been demonstrated across a large number of studies. However, most existing works in VLN-CE focus primarily on transferring the standard discrete vision-and-language navigation (VLN) methods to continuous environments, overlooking the problem of collisions. Such oversight often results in the agent deviating from the planned path or, in severe instances, the agent being trapped in obstacle areas and failing the navigational task. To address the above-mentioned issues, this letter investigates various collision scenarios within VLN-CE and proposes a classification method to predicate the underlying causes of collisions. Furthermore, a new VLN-CE algorithm, named Safe-VLN, is proposed to bolster collision avoidance capabilities including two key components, i.e., a waypoint predictor and a navigator. In particular, the waypoint predictor leverages a simulated 2D LiDAR occupancy mask to prevent the predicted waypoints from being situated in obstacle-ridden areas. The navigator, on the other hand, employs the strategy of ‘re-selection after collision’ to prevent the robot agent from becoming ensnared in a cycle of perpetual collisions. The proposed Safe-VLN is evaluated on the R2R-CE, the results of which demonstrate an enhanced navigational performance and a statistically significant reduction in collision incidences. Copyright © 2023, The Authors. All rights reserved.

关键词： Navigation

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Brain Emotional Learning based Intelligent Controller for a Cable-Driven Parallel Robot

Brain Emotional Learning based Intelligent Controller for a ...

引用

RSI/ISM International Conference on robotics and Mechatronics (ICROM)

作者： Mohammad Bajelani S. Ahmad Khalilpour M. Isaac Hosseini Hamid D. Taghirad Philippe Cardou Advanced Robotics and Automated Systems (ARAS) Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Robotics Laboratory Laval University Quebec City Canada

ISBN: (纸本)9781665420952

Concerning the lack of knowledge about non- linearity and uncertainties existing in the cable-driven robot models, an intelligent controller is proposed in this paper to overcome the lack of knowledge. Brain Emotional Learning is one of the bio-inspired algorithms which mimics the emotional part of the mammals’ brain. Not only does the Brain Emotional Learning Based Intelligent Controller (BELBIC) enable us to reach quick adaptation and robustness, but the computations are also very efficient. By defining the BELBIC learning functions with saturation functions, it is shown that the need to calculate the Jacobian matrix and forward kinematics in the feedback loop is eliminated, while guaranteeing positive tensions to the robot. The performance of the proposed method is examined by experiments, and results show that BELBIC can perform well in terms of tracking error.

关键词： Jacobian matrices Parallel robots Uncertainty Mechatronics Trajectory tracking MIMICs Linearity

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Robust LMI-Based State-Feedback Control of a Knee Exoskeleton Robotic System

Robust LMI-Based State-Feedback Control of a Knee Exoskeleto...

引用

IEEE SSD International Multi-Conference on systems, Signals and Devices

作者： Sahar Jenhani Hassène Gritli Laboratory of Robotics Informatics and Complex Systems (RISC Lab - LR16ES07) National Engineering School of Tunis University of Tunis El Manar Tunis Tunisia Higher Institute of Information and Communication Technologies & RISC Lab (LR16ES07) University of Carthage Tunis Tunisia

Neurological conditions such as strokes, spinal cord injuries, and skeletal muscle weakness can significantly impair an older person’s ability to carry out essential everyday chores. Exoskeletons, also known as wearable technology, are becoming more popular as a way to help the elderly and people with limb disorders move around and recover. In this paper, we focus on the lower limb exoskeletons that are mainly used for physical movement assistance and rehabilitation. Our main objective is to control the actuated lower limb orthosis at the knee joint level. Then, to achieve this goal, we will rely on the dynamics of the shank-orthosis system, which is described via a second-order nonlinear model that takes into account solid and viscous frictions. Thus, in order to robustly stabilize the knee exoskeleton, we introduce a state-feedback control law. Moreover, we use the Schur complement lemma, the Young inequality, and the S-procedure lemma to design LMI conditions, guaranteeing the robust control of the angular position of the knee exoskeleton robotic system. Some simulation results are conducted showing the robustness against external perturbations and unknown friction parameters.

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A colorimetric-bionic sensor for multimodal molecular sensing using hierarchical metamaterials

引用

Device 2025年第5期3卷

作者： Geng, Wenhui Zhou, Ying Huang, Kang Ying, Yibin Xie, Lijuan College of Biosystems Engineering and Food Science Zhejiang University 866 Yuhangtang Road Hangzhou 310058 China The National Key Laboratory of Agricultural Equipment Technology Beijing 100083 China Zhejiang Key Laboratory of Intelligent Sensing and Robotics for Agriculture Hangzhou 310058 China Hangzhou Customs Technology Center 398 Jianshe San Road Xiaoshan District Hangzhou City China Department of Biological Systems Engineering Washington State University Pullman 99164 WA United States

Metamaterials can enhance a local electric field for molecular sensing. However, the difficulty in specifically recognizing target molecules and integrating multiple functionalities restricts their development. Here, we present a hierarchical metamaterial-based colorimetric-bionic sensor (HMm-CBs) using a layer-by-layer assembly method for multimodal molecular sensing. The CBs layers offer molecular recognition ability by enabling the specific perception of analytes, and the HMm layers provide coupling of vibrations between analyte molecules at the terahertz (THz) frequency. The HMm-CBs have broadband adaptability in controlling electromagnetic waves from optical to THz frequency. As a proof of principle, we designed a sensor for analyzing tetracycline hydrochlorides (TCHs). The sensor, in conjunction with a back propagation neural network (BPNN), identifies four types of TCHs. As a demonstration, a multichannel indicator is created for real-time monitoring of shrimp freshness and volatile metabolites. © 2025 The Author(s)

关键词： antibiotics carbon nanotubes chemometric algorithms colorimetric sensor array DTI-3: Develop metamaterials

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A Letter on Progress Made on Husky Carbon: A Legged-Aerial, Multi-modal Platform

arXiv

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arXiv 2022年

作者： Salagame, Adarsh Manjikian, Shoghair Wang, Chenghao Krishnamurthy, Kaushik Venkatesh Pitroda, Shreyansh Gupta, Bibek Jacob, Tobias Mottis, Benjamin Sihite, Eric Ramezani, Milad Ramezani, Alireza The Department of Electrical and Computer Engineering Northeastern University SiliconSynapse Laboratory BostonMA United States The Department of Aerospace Engineering California Institute of Technology PasadenaCA United States The Robotics and Autonomous Systems Group Data61 CSIRO BrisbaneQLD Australia

Animals, such as birds, widely use multi-modal locomotion by combining legged and aerial mobility with dominant inertial effects. The robotic biomimicry of this multi-modal locomotion feat can yield ultra-flexible systems in terms of their ability to negotiate their task spaces. The main objective of this paper is to discuss the challenges in achieving multi-modal locomotion, and to report our progress in developing our quadrupedal robot capable of multi-modal locomotion (legged and aerial locomotion), the Husky Carbon. We report the mechanical and electrical components utilized in our robot, in addition to the simulation and experimentation done to achieve our goal in developing a versatile multi-modal robotic platform. Copyright © 2022, The Authors. All rights reserved.

关键词： Antennas

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Tactile grasp stability classification based on graph convolutional networks

Tactile grasp stability classification based on graph convol...

引用

2021 IEEE International Conference on Real-Time Computing and robotics, RCAR 2021

作者： Mi, Tingting Que, Dashun Fang, Senlin Zhou, Zhenning Ye, Chaoxiang Liu, Chengliang Yi, Zhengkun Wu, Xinyu School of Information Engineering Wuhan University of Technology Wuhan430070 China Shenzhen Institute of Advanced Technology CAS Key Laboratory of Human-Machine Intelligence-Synergy Systems Shenzhen518055 China Guangdong Provincial Key Lab of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Shenzhen Key Laboratory of Smart Sensing and Intelligent Systems Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China SIAT Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518055 China

ISBN: (纸本)9781665436786

One of the challenges for robots to grasp unknown objects is to predict whether objects will fall at the beginning of grasping. Evaluating robotic grasp state accurately and efficiently is a significant step to address this issue. In this paper, based on the different fusion approaches of multi-sensor tactile signals, we propose two novel methods based on Graph Convolution Network (GCN) for robotic stability classification. Specifically, we propose two deep learning methods including GCN based on data-level fusion (GCN-DF) and GCN based on feature-level fusion (GCN-FF). We explore the optimal parameters for transforming sensor signals into a graph structure. Furthermore, we verify the effectiveness of the proposed methods on the BioTac Grasp Stability (BiGS) dataset. The experimental results prove that the proposed approaches achieve higher classification accuracy than Support Vector Machine (SVM) and Long Short-Term Memory (LSTM). © 2021 IEEE.

关键词： Convolution

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A Composite Robust Position Control Design for an Actuated Knee Rehabilitation Exoskeleton

A Composite Robust Position Control Design for an Actuated K...

引用

IEEE SSD International Multi-Conference on systems, Signals and Devices

Exoskeletons or orthotics, also known as wearable technology, are often used to help the elderly and handicapped humans with limb disability to recover their physical mobility. In this work, we are interested in knee exoskeletons employed for the rehabilitation purpose. Our main goal is to control the exoskeleton at the knee joint level by considering external disturbances. Thus, to achieve this robust stabilization, we introduce a linear state-feedback control law combined with a nonlinear controller. The design of the linear control law is mainly relying on the LMI methodology. Simulation results on the robotic system are presented at the end of this paper to prove the performance of the suggested composite controller.

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